Family Violence among Older Adult Patients Consulting in Primary Care Clinics: Results from the ESA (Enquête sur la santé des aînés) Services Study on Mental Health and Aging
Bibliographic record
Abstract
OBJECTIVE: To document the reliability and construct validity of the Family Violence Scale (FVS) in the older adult population aged 65 years and older. METHOD: Data came from a cross-sectional survey, the Enquête sur la santé des aînés et l'utilisation des services de santé (ESA Services Study), conducted in 2011-2013 using a probabilistic sample of older adults waiting for medical services in primary care clinics (n = 1765). Family violence was defined as a latent variable, coming from a spouse and from children. RESULTS: A model with 2 indicators of violence; that is, psychological and financial violence, and physical violence, adequately fitted the observed data. The reliability of the FVS was 0.95. According to our results, 16% of older adults reported experiencing some form of family violence in the past 12 months of their interview, and 3% reported a high level of family violence (FVS > 0.36). Our results showed that the victim's sex was not associated with the degree of violence (β = 0.02). However, the victim's age was associated with family violence (β = -0.12). Older adults, aged 75 years and older, reported less violence than those aged between 65 and 74 years. CONCLUSION: Our results lead us to conclude that family violence against older adults is common and warrants greater public health and political attention. General practitioners could play an active role in the detection of violence among older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".